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E-book
Author Rao, Sunil (Sunil Srinivasa Manjanbail), author

Title Machine learning for solar array monitoring, optimization, and control / Sunil Rao, Sameeksha Katoch, Vivek Narayanaswamy, Gowtham Muniraju, Cihan Tepedelenlioglu, Andreas Spanias, Pavan Turaga, Raja Ayyanar, and Devarajan Srinivasan
Published Cham, Switzerland : Springer, [2020]
©2020

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Description 1 online resource (ix, 81 pages) : illustrations (some color)
Series Synthesis lectures on power electronics, 1931-9533 ; #13
Synthesis lectures on power electronics ; #13.
Contents 1. Introduction -- 2. Solar array research testbed -- 2.1. The SenSIP 18 kW solar array testbed -- 2.2. Design of the solar array testbed -- 2.3. The MATLAB simulink model -- 2.4. The PVWatts dataset -- 2.5. Summary
3. Fault classification using machine learning -- 3.1. Faults in PV arrays -- 3.2. Standard machine learning algorithms -- 3.3. Neural networks -- 3.4. Fault detection and computational complexity -- 3.5. Graph signal processing -- 3.6. Semi-supervised graph-based classification -- 3.7. Summary
4. Shading prediction for power optimization -- 4.1. Partial shading on photovoltaic panels -- 4.2. Prior work in cloud motion and dynamic texture synthesis -- 4.3. Dynamic texture prediction model -- 4.4. Simulation results -- 4.5. Shading and topology reconfiguration -- 4.6. Summary
5. Topology reconfiguration using neural networks -- 5.1. Need for topology reconfiguration -- 5.2. Prior work -- 5.3. Machine learning for topology reconfiguration -- 5.4. Methodology -- 5.5. Empirical evaluations -- 5.6. Summary -- 6. Summary
Summary The efficiency of solar energy farms requires detailed analytics and information on each panel regarding voltage, current, temperature, and irradiance. Monitoring utility-scale solar arrays was shown to minimize the cost of maintenance and help optimize the performance of the photo-voltaic arrays under various conditions. We describe a project that includes development of machine learning and signal processing algorithms along with a solar array testbed for the purpose of PV monitoring and control. The 18kW PV array testbed consists of 104 panels fitted with smart monitoring devices. Each of these devices embeds sensors, wireless transceivers, and relays that enable continuous monitoring, fault detection, and real-time connection topology changes. The facility enables networked data exchanges via the use of wireless data sharing with servers, fusion and control centers, and mobile devices. We develop machine learning and neural network algorithms for fault classification. In addition, we use weather camera data for cloud movement prediction using kernel regression techniques which serves as the input that guides topology reconfiguration. Camera and satellite sensing of skyline features as well as parameter sensing at each panel provides information for fault detection and power output optimization using topology reconfiguration achieved using programmable actuators (relays) in the SMDs. More specifically, a custom neural network algorithm guides the selection among four standardized topologies. Accuracy in fault detection is demonstrate at the level of 90+% and topology optimization provides increase in power by as much as 16% under shading
Analysis deep learning
photovoltaic systems
machine learning
neural networks
PV topology optimization
solar panel shading
solar array fault detection
graph signal processing
PV inverters
smart grid
computer vision in PV
Bibliography Includes bibliographical references (pages 65-78)
Notes Title from PDF title page (viewed on September 8, 2020)
Subject Photovoltaic power systems -- Automatic control
Intelligent control systems.
Machine learning.
Intelligent control systems
Machine learning
Form Electronic book
Author Katoch, Sameeksha, author.
Narayanaswamy, Vivek, author
Muniraju, Gowtham, author
Tepedelenlioğlu, Cihan, author.
Spanias, Andreas, author.
Turaga, Pavan, author.
Ayyanar, Raja, author.
Srinivasan, Devarajan, author
ISBN 9781681739083
1681739089
9783031025051
3031025059